Deep mining data management method for full life cycle of underground engineering

Abstract

The invention provides a deep mining data management method for the full life cycle of underground engineering, and relates to the technical field of data processing, and the method comprises the steps: surveying a target underground engineering, and if the target geological information meets the preset geological requirements of the target engineering design information, determining that the target engineering design information does not meet the preset geological requirements; dynamically monitoring the construction process of the target underground engineering based on the target engineering design information, detecting first construction monitoring data of first construction monitoring equipment in the construction monitoring information, generating target construction information, and constructing a target digital model by the target construction information and target geological information; and rendering the monitored target maintenance information to the target digital model for visual management, thereby solving the technical problem of low construction safety and quality caused by insufficient management and control of the construction technology and technical measures in the underground construction process in the prior art. The construction technology and technical measures in the underground construction process are reasonably and accurately managed and controlled, and the construction safety and quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method for managing deep mining data throughout the entire life cycle of underground engineering. Background Art

[0002] With the development of science and technology, especially the development of underground excavation engineering, the burial depth of underground mining is bound to increase. The expansion of mining scale and the improvement of mechanization level have accelerated the deep development of underground. In the process of underground engineering, it is necessary to consider the limitation of the entire life cycle of underground engineering. However, due to the insufficient control of construction technology and technical measures in the underground construction process in the existing technology, technical problems such as low construction safety and quality arise. Summary of the invention

[0003] The present application provides a deep mining data management method for the entire life cycle of underground engineering, which is used to solve the technical problems existing in the prior art of insufficient control over construction processes and technical measures during underground construction, resulting in low construction safety and quality.

[0004] In view of the above problems, this application provides a deep mining data management method for the entire life cycle of underground engineering.

[0005] In a first aspect, the present application provides a method for managing deep mining data throughout the life cycle of a downhole project, the method comprising: retrieving target engineering design information of a target downhole project; surveying the target downhole project through distributed geological survey equipment in a distributed deep mining equipment group to obtain target geological information; determining whether the target geological information meets predetermined geological requirements of the target engineering design information; if so, starting distributed construction monitoring equipment in the distributed deep mining equipment group to dynamically monitor the construction process of the target downhole project based on the target engineering design information to obtain construction monitoring information; introducing a preset data detection function to detect first construction monitoring data of a first construction monitoring equipment in the construction monitoring information, and generating target construction information based on the detection result; constructing a target digital model of the target downhole project based on the target geological information and the target construction information; rendering target maintenance information monitored by distributed maintenance monitoring equipment in the distributed deep mining equipment group to the target digital model, the target digital model being used to visualize the deep mining data of the target downhole project.

[0006] In the second aspect, the present application provides a deep mining data management system for the entire life cycle of a downhole project, the system comprising: a retrieval module, the retrieval module is used to retrieve the target engineering design information of the target downhole project; a survey module, the survey module is used to survey the target downhole project through the distributed geological survey equipment in the distributed deep mining equipment group to obtain the target geological information; a first judgment module, the first judgment module is used to judge whether the target geological information meets the predetermined geological requirements of the target engineering design information; a dynamic monitoring module, the dynamic monitoring module is used to start the distributed construction monitoring equipment in the distributed deep mining equipment group to survey the target downhole project based on the target engineering design information if the requirements are met. Dynamically monitor the construction process of the project to obtain construction monitoring information; a detection module, the detection module is used to introduce a preset data detection function to detect the first construction monitoring data of the first construction monitoring equipment in the construction monitoring information, and generate target construction information according to the detection result; a model building module, the model building module is used to build a target digital model of the target underground project based on the target geological information and the target construction information; a visualization management module, the visualization management module is used to render the target maintenance information monitored by the distributed maintenance monitoring equipment in the distributed deep mining equipment group to the target digital model, and the target digital model is used to visualize the deep mining data of the target underground project.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The deep mining data management method for the entire life cycle of underground projects provided in this application relates to the field of data processing technology. It solves the technical problems in the prior art of insufficient control over construction technology and technical measures in the underground construction process, resulting in low construction safety and quality. It realizes reasonable and precise control over construction technology and technical measures in the underground construction process, thereby improving construction safety and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of the deep mining data management method for the entire life cycle of underground engineering is provided for this application; Figure 2 A schematic diagram of the process of adjusting target engineering design information in a deep mining data management method for the entire life cycle of underground engineering is provided for this application; Figure 3 A schematic diagram of the target construction information flow in the deep mining data management method for the entire life cycle of underground engineering is provided for this application; Figure 4 A structural diagram of the deep mining data management system for the entire life cycle of underground engineering is provided for this application.

[0009] Explanation of the accompanying drawings: retrieval module 1, survey module 2, first judgment module 3, dynamic monitoring module 4, detection module 5, model building module 6, visualization management module 7. DETAILED DESCRIPTION

[0010] This application provides a deep mining data management method for the entire life cycle of underground engineering, which is used to solve the technical problems in the existing technology of insufficient control over construction processes and technical measures during underground construction, resulting in low construction safety and quality.

[0011] Embodiment 1 like Figure 1 As shown, the embodiment of the present application provides a deep mining data management method for the entire life cycle of underground engineering. The method is applied to a deep mining data management system for the entire life cycle of underground engineering. The system is communicatively connected with a distributed deep mining equipment group. The method includes: Step A100: Retrieving target engineering design information of a target underground engineering; In the present application, the deep mining data management method for the whole life cycle of the underground project provided by the embodiment of the present application and the deep mining data management system for the whole life cycle of the underground project, in order to ensure the accuracy of the deep mining data management of the target underground project in the later stage, it is first necessary to retrieve the target project design information based on the basic data of the target underground project, and the basic data of the target underground project may include the depth data of the underground project, the temperature data of the underground project, the water level data of the underground project, the pressure data of the underground project, etc. At the same time, the target project design information is extracted based on the basic data contained in the target underground project, and the target underground project design information is used to improve the operational efficiency of the underground project and ensure the safety of the underground project. In the process of implementing the underground project, each decision should be made according to certain design principles to avoid errors and problems, which serves as an important reference for the later implementation of deep mining data management for the whole life cycle of the underground project.

[0012] Step A200: surveying the target underground project by using the distributed geological survey equipment in the distributed deep mining equipment group to obtain target geological information; In this application, in order to improve the efficiency of the later underground engineering construction and management, it is necessary to survey the target underground engineering through the distributed geological survey equipment in the distributed deep mining equipment group. The deep mining data management system of the underground engineering throughout its life cycle is connected to the distributed deep mining equipment group in communication. The distributed deep mining equipment group is used to collect underground deep environmental parameters, and the distributed geological survey equipment in the distributed deep mining equipment group can include handheld field ore element analyzers, geological sampling equipment, geochemical exploration instruments, drilling instruments, geophysical exploration instruments, remote sensing measurement instruments, etc. It is used to survey the geological information in the target underground engineering, and the survey results refer to the material composition, structure, and structure, which can include physical properties, chemical properties, rock properties, mineral composition and other information in the underground geology, so as to determine the geological information in the underground engineering, that is, the target geological information, and provide an important reference for the construction and management of the underground engineering through the target geological information.

[0013] Step A300: determining whether the target geological information meets the predetermined geological requirements of the target engineering design information; Furthermore, if Figure 2 As shown, step A300 of the present application also includes: Step A310: If not satisfied, extract target lithology information and target hydrological information from the target geological information, wherein the target lithology information includes target rock hardness and target rock structure, and the target hydrological information includes target groundwater type and target groundwater level; Step A320: performing standardization processing on the target rock hardness, the target rock structure, the target groundwater type and the target groundwater level to obtain target standardized geological information; Step A330: weighting the target standardized geological information to calculate a target geological comprehensive index; Step A340: Adjust the target engineering design information based on the predetermined engineering design scheme corresponding to the target geological comprehensive index.

[0014] In this application, in order to more accurately perform deep mining data management throughout the entire life cycle of the underground project, it is first necessary to judge the underground geological information in the underground project, that is, to judge whether the target geological information determined above meets the predetermined geological requirements of the retrieved target engineering design information. The predetermined geological requirements of the target engineering design information refer to the geological information that meets the engineering progress during the engineering design carried out underground.

[0015] If the target geological information meets the predetermined geological requirements of the target engineering design information, the distributed construction monitoring equipment in the distributed deep excavation equipment group is started to dynamically monitor the construction process of the target underground project based on the target engineering design information to obtain construction monitoring information.

[0016] If the target geological information does not meet the predetermined geological requirements of the target engineering design information, it is necessary to extract the target lithology information and target hydrological information in the target geological information, and the target lithology information includes the target rock hardness and target rock structure. The target rock hardness can be the ability of the rock to resist being scratched or pressed into its surface by other objects, and its unit of measurement is Pa or MPa. The target rock structure can be the characteristics manifested by the degree of crystallization of the material constituting the rock, the size of the mineral particles, the shape of the minerals, and the relationship between them. The target hydrological information includes the target groundwater type and the target groundwater level. The target groundwater type can be the water existing in the gaps in the rock strata below the surface, and the target groundwater level can be the elevation of the groundwater surface relative to the reference surface.

[0017] Further standardization of the target rock hardness, target rock structure, target groundwater type and target groundwater level refers to classifying and organizing the target rock hardness, target rock structure, target groundwater type and target groundwater level according to the downhole rock hardness standard, rock structure standard, groundwater type standard and groundwater level standard in the big data to facilitate data analysis and decision-making, thereby obtaining the target standardized geological information, and performing weighted calculation on the target standardized geological information on this basis.

[0018] Weighted calculation needs to be based on a large amount of data aggregation and accurate determination of weights before targeted calculation. For example, the weight ratio of rock hardness standard information, rock structure standard information, groundwater type standard information and groundwater level standard information in the target standardized geological information can be the first influence coefficient: the second influence coefficient: the third influence coefficient: the fourth influence coefficient is 4:2:3:2. The influencing parameters after the weighted calculation process are the first influence parameter*0.4, the second influence parameter*0.2, the third influence parameter*0.3, and the fourth influence parameter*0.2. The target geological comprehensive index is obtained according to the weighted calculation result. Finally, the predetermined engineering design plan corresponding to the target geological comprehensive index is obtained. The predetermined engineering design plan is obtained by matching different geological conditions with different construction processes and technical measures. For example, when the target rock hardness is hard rock, hard rock is not easy to deform or damage due to its high strength and low compressibility. When conducting underground construction in hard rock, it is necessary to pay attention to the use of high-efficiency drilling and rock breaking methods, such as using impact drills, pneumatic drills, laser drills, etc., as well as controlling the drilling pressure and rotation speed to avoid excessive impact and friction on the rock, prevent rock bursting or falling off, and then strengthen cooling and lubrication to keep the drill bit cool and lubricated, reduce drill wear and the occurrence of drill sticking accidents, and at the same time strengthen on-site monitoring and geological forecasts, timely grasp the mechanical properties and geological structure of the rock, and take corresponding measures.

[0019] Furthermore, adjusting the target engineering design information according to the predetermined engineering design scheme means that when the predetermined engineering design scheme does not meet the actual conditions of the underground project, it is deemed that there are design problems in the predetermined engineering design scheme, and it is necessary to compare the engineering design data in the predetermined engineering design scheme with the actual underground delayed hardness, rock structure, groundwater type, and groundwater level in the same way as above, so as to make corresponding data adjustments to the target engineering design information. When the predetermined engineering design scheme meets the actual conditions of the underground project, construction will be carried out, thereby ensuring the realization of deep mining data management for the entire life cycle of the underground project.

[0020] Step A400: If the conditions are met, start the distributed construction monitoring equipment in the distributed deep excavation equipment group to dynamically monitor the construction process of the target underground project based on the target project design information to obtain construction monitoring information; In the present application, if the target geological information meets the predetermined geological requirements of the target engineering design information, the distributed construction monitoring equipment in the distributed deep excavation equipment group is started to dynamically monitor the construction process of the target downhole engineering based on the target engineering design information. The distributed construction monitoring equipment in the distributed deep excavation equipment group is used to perform real-time dynamic monitoring of downhole parameters during the downhole engineering construction process. The data that can be monitored by the distributed construction monitoring equipment include downhole engineering depth monitoring data, downhole engineering temperature monitoring data, downhole engineering water level monitoring data, downhole engineering pressure monitoring data, etc. The data monitored are all real-time dynamically changing data. On this basis, all the monitored data are integrated and recorded as construction monitoring information for output, which lays a solid foundation for the subsequent realization of deep mining data management for the entire life cycle of the downhole engineering.

[0021] Step A500: introducing a preset data detection function to detect the first construction monitoring data of the first construction monitoring equipment in the construction monitoring information, and generating target construction information according to the detection result; Furthermore, if Figure 3 As shown, step A500 of the present application also includes: Step A510: acquiring devices of the same type as the first construction monitoring device from the distributed construction monitoring devices, and forming a first set of devices of the same type; Step A520: Acquire the first similar equipment construction monitoring data of the first similar equipment in the first similar equipment set; Step A530: Calculating and obtaining a first support degree of the first similar equipment construction monitoring data to the first construction monitoring data based on the preset data detection function; Step A540: If the first support does not reach a predetermined support threshold, the first construction monitoring data is used as the first candidate abnormal data; Step A550: reversely checking and determining whether the first construction monitoring device of the first candidate abnormal data is abnormal; Step A560: If the first construction monitoring device has no abnormality, calculate a first deviation between the construction monitoring data of the first similar device and the first construction monitoring data; Step A570: Determine whether the first deviation is within a predetermined deviation threshold, and if so, add the first candidate abnormal data to the target construction information, wherein the target construction information is stored in a data warehouse.

[0022] In the present application, in order to improve the construction efficiency of underground engineering during the construction process, it is necessary to introduce a preset data detection function to detect the construction monitoring information obtained by the above monitoring, which refers to randomly extracting a monitoring device in the construction monitoring information, setting it as the first construction monitoring device, and recording the construction monitoring data obtained by the first construction monitoring device as the first construction monitoring data, and further determining multiple similar devices of the first construction monitoring device according to the device type of the first construction monitoring device, which refers to the same type of equipment as the first construction monitoring device in the distributed construction monitoring device, and recording it as the first similar equipment set, so as to extract the corresponding first similar equipment construction monitoring data according to the first similar equipment contained in the first similar equipment set, and further, according to the introduced preset data detection function, the first support of the first similar equipment construction monitoring data to the first construction monitoring data is calculated, and the expression of the preset data detection function is as follows: ; in, Refers to the construction monitoring data of the first similar equipment The first construction monitoring data The first support of , k refers to the amplitude of the preset data detection function, and , is the attenuation factor, and .

[0023] It means that after subtracting the first construction monitoring data from the first similar equipment construction monitoring data, the data changes according to the amplitude of the preset data detection function. The amplitude of the preset data detection function is inversely proportional to the calculated first support. The higher the amplitude of the preset data detection function, the lower the first support. Further, the first support and the predetermined support threshold are judged. The predetermined support threshold is set according to the average support of the equipment construction monitoring data to the construction monitoring data in the historical period. If the first support does not reach the predetermined support threshold, it is deemed that there is a hidden danger in the first construction monitoring data, and the first construction monitoring data is used as the first candidate abnormal data, so as to reversely check and determine whether the first construction monitoring equipment of the first candidate abnormal data is abnormal. If the first construction monitoring equipment is abnormal, the first candidate abnormal data is eliminated. For all the data monitored by a construction monitoring device, if the first construction monitoring device shows no abnormality, the construction monitoring data of the first similar device is subtracted from the first construction monitoring data, and the result of the subtraction is recorded as the first deviation. Further, it is determined whether the first deviation is within the predetermined deviation threshold. The predetermined deviation threshold is determined based on the extreme value of the data deviation in the big data. If the first deviation is within the predetermined deviation threshold, it means that through this analysis and judgment, the first candidate abnormal data actually belongs to the normal situation, that is, the non-abnormal situation. Therefore, the first candidate abnormal data is saved as valid data, and the first candidate abnormal data is added to the target construction information at the same time. The target construction information is stored in the data warehouse to facilitate the subsequent data retrieval, which has a limited role in realizing in-depth data mining and management over the entire life cycle of underground engineering.

[0024] Furthermore, step A570 of the present application includes: Step A571: if the first deviation is not within the predetermined deviation threshold, taking the first candidate abnormal data as the first abnormal data; Step A572: traversing the first indicator type of the first abnormal data in the underground construction accident tree in the first similar device to obtain a first predicted construction risk, wherein the underground construction accident tree is an accident tree constructed by analyzing risk accident records in historical underground construction records; Step A573: Perform emergency management and control on the first predicted construction risk.

[0025] In the present application, it is determined whether the first deviation is within a predetermined deviation threshold. If the first deviation is not within the predetermined deviation threshold, the first candidate abnormal data is used as the first abnormal data, and is synchronously added to the target construction information. Furthermore, in a first similar device, the first indicator type of the first abnormal data is traversed in the underground construction accident tree. The underground construction accident tree is an accident tree constructed by connecting data nodes through analysis of risk accident records in historical underground construction records, and the first indicator type of the first abnormal data is traversed in the underground construction accident tree. Edge computing is performed on the first indicator type of the first abnormal data. On the side close to the first indicator type of the first abnormal data, an open platform integrating network, computing, storage, and application core capabilities is used to provide the nearest service nearby. Its application is initiated on the edge side, generating faster network service response, improving the real-time performance of anomaly detection and analysis, and the response speed and timeliness of emergency handling of the deep mining data management system throughout the life cycle of underground engineering. On this basis, the first predicted construction risk is generated according to the abnormal data and the type of abnormal data, and the first predicted construction risk is treated with emergency management at the same time. Its emergency management can be to formulate a complete emergency plan for underground engineering, build an emergency organization, strengthen emergency drills, and implement emergency equipment and other measures. For example, if the first predicted construction risk is groundwater, the impact on underground construction is mainly manifested in the following aspects: First, reduce the strength of the surrounding rock: groundwater can soften the surrounding rock, reduce its strength and stability, and easily cause accidents such as roof collapse and spalling. Therefore, when conducting underground construction in areas with groundwater, effective waterproofing measures need to be taken. Second, increase the difficulty of construction: the presence of groundwater will increase the difficulty of construction, such as the workload of mud treatment and drainage. At the same time, groundwater will also affect the direction and depth of the borehole, requiring special treatment. Third, it threatens the safety of workers: groundwater may contain harmful substances, such as hydrogen sulfide and methane, which may threaten the health of workers. Therefore, when conducting underground construction in areas with groundwater, effective ventilation and protection measures need to be taken to serve as reference data for in-depth data management of the entire life cycle of underground projects.

[0026] Step A600: constructing a target digital model of the target underground engineering based on the target geological information and the target construction information; In this application, in order to ensure more accurate management of the deep mining data of the underground project in the later stage, it is necessary to construct a target digital model of the target underground project by using the target geological information and the target construction information as the infrastructure data. The model processing flow of the target digital model includes: A fully connected neural network is constructed and trained using target geological information and target construction information. The fully connected neural network refers to a neural network with a multi-layer perceptron structure. The target digital model is further constructed, and each node of each layer of the fully connected neural network is connected to all nodes in the upper and lower layers. The target digital model includes an input layer, a hidden layer, and an output layer. The input layer is a layer for data input, the hidden layer is used to better separate data features, and the output layer is a layer for result output. The target digital model is obtained by training with a training data set and a supervision data set, wherein each set of training data in the training data set includes target geological information and target construction information, and the supervision data set is supervision data that corresponds one-to-one to the training data set.

[0027] Furthermore, each group of training data in the training data set is input into the target digital model, and the output supervision adjustment of the target digital model is performed through the supervision data corresponding to this group of training data. When the output result of the target digital model is consistent with the supervision data, the training of the current group is completed, and the training of all the training data in the training data set is completed, then the training of the fully connected neural network is completed.

[0028] In order to ensure the convergence and accuracy of the target digital model, the convergence process can be that when the output data in the target digital model converges to a point and approaches a certain value, it is considered convergence. Its accuracy can be tested by means of a test data set to process the target digital model. For example, the test accuracy can be set to 80%. When the test accuracy of the test data set meets 80%, the target digital model is constructed, thereby improving the accuracy of deep mining data management for the entire life cycle of underground engineering in the later stage.

[0029] Step A700: Rendering target maintenance information monitored by the distributed maintenance monitoring equipment in the distributed deep mining equipment group to the target digital model, wherein the target digital model is used for visual management of deep mining data of the target underground engineering.

[0030] Furthermore, step A700 of the present application also includes: Step A710: copy the target maintenance information to the data warehouse platform; Step A720: Generate a target maintenance decision for the target downhole project based on the data in the data warehouse.

[0031] Furthermore, step A700 of the present application also includes: Step A730: simulating the target downhole engineering based on the target digital model to obtain target simulation information; Step A740: extracting target groundwater information from the target simulation information, wherein the target groundwater information refers to information on the impact of the target underground project on groundwater during operation; Step A750: Generate a target anti-seepage remediation plan based on the target groundwater information, and the target anti-seepage remediation plan is used to remediate the groundwater impact on the target underground project.

[0032] In the present application, the target maintenance information obtained by monitoring the construction of the underground project by the distributed maintenance monitoring equipment in the distributed deep mining equipment group that is communicated with the deep mining data management system of the entire life cycle of the underground project is rendered into the target digital model constructed above, which means that the maintenance parameters in the target maintenance information are synchronously copied to the data warehouse middle platform. That is, after the underground project is completed, it is necessary to set the regular maintenance management parameters in the actual operation process, and it is necessary to frequently call the historical maintenance information and generate the prediction information and alarm information of the next maintenance. Therefore, it is necessary to store the maintenance information in the data warehouse middle platform to facilitate data call and thus improve data retrieval efficiency. Furthermore, according to the maintenance data stored in the data warehouse middle platform, a target maintenance decision for the target underground project is generated, which means the decision determined after serializing the maintenance data according to the maintenance time sequence and maintenance weight.

[0033] Furthermore, the deep mining data of the target underground project is visualized and managed through the target digital model. The process may be to simulate the target underground project in the target digital model, use real underground construction equipment as the control platform, and use virtual reality technology to perform actual operations to obtain target simulation information. Furthermore, in order to avoid water seepage in the underground project during actual operation, the target groundwater information in the target simulation information obtained by the simulation is extracted. The target groundwater information refers to the information on the impact of the target underground project on groundwater during operation. The impact of groundwater may include the impact on construction personnel, construction equipment, construction progress, etc. Furthermore, a target anti-seepage remediation plan is generated based on the seepage point information, anti-seepage coating information, seepage degree information, seepage geological information, etc. in the target groundwater information. Finally, the target underground project is remedied for the impact of groundwater through the target anti-seepage remediation plan, so as to ensure better deep mining data management for the entire life cycle of the underground project in the later stage.

[0034] To sum up, the deep mining data management method for the entire life cycle of underground engineering provided in the embodiment of the present application includes at least the following technical effects, which realizes the rational and precise control of construction processes and technical measures during underground construction, and improves construction safety and quality.

[0035] Embodiment 2 Based on the same inventive concept as the method for managing deep mining data throughout the life cycle of underground engineering in the aforementioned embodiment, Figure 4 As shown, this application provides a deep mining data management system for the entire life cycle of underground engineering, and the system includes: A retrieval module 1, wherein the retrieval module 1 is used to retrieve target engineering design information of a target downhole engineering; A survey module 2, the survey module 2 is used to survey the target underground project through the distributed geological survey equipment in the distributed deep mining equipment group to obtain target geological information; A first judgment module 3, the first judgment module 3 is used to judge whether the target geological information meets the predetermined geological requirements of the target engineering design information; Dynamic monitoring module 4, the dynamic monitoring module 4 is used to start the distributed construction monitoring equipment in the distributed deep excavation equipment group to dynamically monitor the construction process of the target underground project based on the target project design information if the conditions are met, and obtain construction monitoring information; A detection module 5, the detection module 5 is used to introduce a preset data detection function to detect the first construction monitoring data of the first construction monitoring equipment in the construction monitoring information, and generate target construction information according to the detection result; A model building module 6, the model building module 6 is used to build a target digital model of the target underground engineering based on the target geological information and the target construction information; A visualization management module 7 is used to render the target maintenance information monitored by the distributed maintenance monitoring equipment in the distributed deep mining equipment group to the target digital model, and the target digital model is used to visualize the deep mining data of the target underground project.

[0036] Furthermore, the system also includes: A second judgment module, wherein if the conditions are not met, the second judgment module is used to extract target lithology information and target hydrological information from the target geological information, wherein the target lithology information includes target rock hardness and target rock structure, and the target hydrological information includes target groundwater type and target groundwater level; A standardization processing module, the standardization processing module is used to perform standardization processing on the target rock hardness, the target rock structure, the target groundwater type and the target groundwater level to obtain target standardized geological information; A first calculation module, the first calculation module is used to calculate the target geological comprehensive index by weighting the target standardized geological information; A first adjustment module is used to adjust the target engineering design information based on a predetermined engineering design scheme corresponding to the target geological comprehensive index.

[0037] Furthermore, the system also includes: A composition module, wherein the composition module is used to obtain devices of the same type as the first construction monitoring device in the distributed construction monitoring devices and form a first set of similar devices; A data acquisition module, the data acquisition module is used to acquire first similar equipment construction monitoring data of first similar equipment in the first similar equipment set; A second calculation module, the second calculation module is used to calculate the first support degree of the first similar equipment construction monitoring data to the first construction monitoring data based on the preset data detection function; a third judgment module, wherein if the first support does not reach a predetermined support threshold, the first construction monitoring data is used as the first candidate abnormal data; A fourth judgment module, the fourth judgment module is used to reversely check and judge whether the first construction monitoring device of the first candidate abnormal data is abnormal; A third calculation module, the third calculation module is used to calculate a first deviation between the construction monitoring data of the first similar equipment and the first construction monitoring data if the first construction monitoring equipment has no abnormality; A fifth judgment module is used to judge whether the first deviation is within a predetermined deviation threshold, and if so, add the first candidate abnormal data to the target construction information, wherein the target construction information is stored in a data warehouse.

[0038] Furthermore, the system also includes: A sixth judgment module, configured to take the first candidate abnormal data as the first abnormal data if the first deviation is not within the predetermined deviation threshold; A construction module, the construction module is used to traverse the first indicator type of the first abnormal data in the underground construction accident tree in the first similar device to obtain a first predicted construction risk, wherein the underground construction accident tree is an accident tree constructed by analyzing risk accident records in historical underground construction records; A management and control processing module, wherein the management and control processing module is used to perform emergency management and control processing on the first predicted construction risk.

[0039] Furthermore, the system also includes: A copy module, the copy module is used to copy the target maintenance information to the data warehouse platform; A decision generation module is used to generate a target maintenance decision for the target downhole project based on the data stored in the data warehouse.

[0040] Furthermore, the system also includes: A simulation module, the simulation module is used to simulate the target downhole engineering based on the target digital model to obtain target simulation information; An information acquisition module, the information acquisition module is used to extract target groundwater information from the target simulation information, the target groundwater information refers to information on the impact of the target underground project on groundwater during operation; A remedial treatment module is used to generate a target anti-seepage remediation plan based on the target groundwater information, and the target anti-seepage remediation plan is used to perform groundwater impact remediation treatment on the target underground project.

[0041] Through the above-mentioned detailed description of the deep mining data management method for the entire life cycle of underground engineering in this specification, those skilled in the art can clearly know the deep mining data management system for the entire life cycle of underground engineering in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0042] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A deep mining data management method for the entire life cycle of underground engineering, characterized in that: The method comprises: Retrieve target engineering design information of target underground engineering; Surveying the target underground project by using distributed geological survey equipment in the distributed deep mining equipment group to obtain target geological information; Determining whether the target geological information meets the predetermined geological requirements of the target engineering design information; If the conditions are met, the distributed construction monitoring equipment in the distributed deep excavation equipment group is started to dynamically monitor the construction process of the target underground project based on the target project design information to obtain construction monitoring information; Introducing a preset data detection function to detect the first construction monitoring data of the first construction monitoring device in the construction monitoring information, and generating target construction information according to the detection result; Constructing a target digital model of the target underground project based on the target geological information and the target construction information; The target maintenance information monitored by the distributed maintenance monitoring equipment in the distributed deep mining equipment group is rendered to the target digital model, and the target digital model is used to perform visual management on the deep mining data of the target underground engineering.

2. The method according to claim 1, characterized in that: After determining whether the target geological information meets the predetermined geological requirements of the target engineering design information, the method further includes: If not, extracting target lithology information and target hydrological information from the target geological information; Wherein, the target lithology information includes target rock hardness and target rock structure, and the target hydrological information includes target groundwater type and target groundwater level; Standardizing the target rock hardness, the target rock structure, the target groundwater type and the target groundwater level to obtain target standardized geological information; The target standardized geological information is weighted to calculate a target geological comprehensive index; The target engineering design information is adjusted based on a predetermined engineering design scheme corresponding to the target geological comprehensive index.

3. The method according to claim 1, characterized in that: The introducing of the preset data detection function to detect the first construction monitoring data of the first construction monitoring device in the construction monitoring information, and generating the target construction information according to the detection result, includes: Acquire devices of the same type as the first construction monitoring device from the distributed construction monitoring devices and form a first set of similar devices; Acquire first similar equipment construction monitoring data of first similar equipment in the first similar equipment set; Calculate the first support degree of the first similar equipment construction monitoring data for the first construction monitoring data based on the preset data detection function; If the first support does not reach a predetermined support threshold, taking the first construction monitoring data as first candidate abnormal data; Reversely check and determine whether the first construction monitoring device of the first candidate abnormal data is abnormal; If the first construction monitoring device has no abnormality, calculating a first deviation between the construction monitoring data of the first similar device and the first construction monitoring data; It is determined whether the first deviation is within a predetermined deviation threshold, and if so, the first candidate abnormal data is added to the target construction information, wherein the target construction information is stored in a data warehouse.

4. The method according to claim 3, characterized in that: The determining whether the first deviation is within a predetermined deviation threshold further includes: If the first deviation is not within the predetermined deviation threshold, taking the first candidate abnormal data as the first abnormal data; In the first similar device, traverse the first indicator type of the first abnormal data in the underground construction accident tree to obtain a first predicted construction risk; Wherein, the underground construction accident tree is an accident tree constructed by analyzing the risk accident records in the historical underground construction records; Perform emergency management and control on the first predicted construction risk.

5. The method according to claim 4, characterized in that: The expression of the preset data detection function is as follows: ； in, Refers to the construction monitoring data of the first similar equipment The first construction monitoring data The first support of , k refers to the amplitude of the preset data detection function, and , is the attenuation factor, and .

6. The method according to claim 3, characterized in that: The data warehouse further includes a data warehouse middle platform, which, after rendering the target maintenance information monitored by the distributed maintenance monitoring equipment in the distributed deep mining equipment group to the target digital model, further includes: Copying the target maintenance information to the data warehouse platform; A target maintenance decision for the target underground project is generated based on the data stored in the data warehouse.

7. The method according to claim 6, characterized in that: The method further comprises: Performing simulation on the target downhole engineering based on the target digital model to obtain target simulation information; Extracting target groundwater information from the target simulation information, wherein the target groundwater information refers to information on the impact of the target underground project on groundwater during operation; A target anti-seepage remediation plan is generated according to the target groundwater information, and the target anti-seepage remediation plan is used to perform groundwater impact remediation treatment on the target underground project.

8. A deep mining data management system for the entire life cycle of underground engineering, characterized by: The system is communicatively connected with a distributed deep mining equipment group, and the system comprises: A retrieval module, the retrieval module is used to retrieve target engineering design information of a target downhole engineering; A survey module, the survey module is used to survey the target underground project through the distributed geological survey equipment in the distributed deep mining equipment group to obtain target geological information; A first judgment module, the first judgment module is used to judge whether the target geological information meets the predetermined geological requirements of the target engineering design information; A dynamic monitoring module, wherein if the conditions are met, the distributed construction monitoring equipment in the distributed deep excavation equipment group is started to dynamically monitor the construction process of the target underground project based on the target project design information to obtain construction monitoring information; A detection module, the detection module is used to introduce a preset data detection function to detect the first construction monitoring data of the first construction monitoring equipment in the construction monitoring information, and generate target construction information according to the detection result; A model building module, the model building module is used to build a target digital model of the target downhole engineering based on the target geological information and the target construction information; A visualization management module, which is used to render the target maintenance information monitored by the distributed maintenance monitoring equipment in the distributed deep mining equipment group to the target digital model, and the target digital model is used to visualize the deep mining data of the target underground project.

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